Clay detection and quantification using downhole low frequency electromagnetic measurements

ABSTRACT

Methods and systems are provided for clay detection, clay typing, and clay volume quantification using downhole electromagnetic measurements conducted by a downhole logging tool on a formation at a low frequency less than 5000 Hz. The downhole electromagnetic measurements are used to determine permittivity data that characterizes permittivity of the formation at the low frequency less than 5000 Hz. The downhole low frequency electromagnetic measurements are nondestructive, and the results indicate it is with high sensitivity to the existence of clays.

FIELD

The present disclosure relates to methods and systems that can detectclay in a rock sample and that can also determine data characterizingclay type and volume fraction of clay in the rock sample.

BACKGROUND

Geological knowledge of formation rock is important for resourcesexploration, field development and production planning. To assessreservoir quality and the amount of hydrocarbons in-place,characterization of the mineralogy of the formation rock is needed inorder to accurately calculate reservoir porosity and hydrocarbonsaturation from logs. This is particularly true in the event that clayis present in the formation rock. Each hydrocarbon reservoir can containdifferent types of clays and different amounts of such clay types. Eachclay type has its own different characteristics, which can be translatedto what is called cation exchange capacity (CEC) in common petrophysicalapplications.

Clays are hydrous aluminum silicate minerals that are platy in structureand can form by the alteration of silicate minerals like feldspar andamphibole. Clays are commonly grouped into a number of clay types,including but not limited smectite, kaolinite, chlorite, illite. Someclays have a tendency to swell when exposed to water, creating apotential drilling hazard when clay-bearing rock formations are exposedto water-base fluids during drilling, possibly reducing the permeabilityof a good reservoir rock. Some clays are used in drilling fluids to forman impermeable mudcake to isolate a formation from the invasion ofdrilling fluid. The structural differences amongst the clay types(smectite, kaolinite, chlorite, illite) can determine the surface areaexposed to reservoir fluids or stimulating fluids. Clays can be found inpore spaces, as part of the matrix or as grain-cementing material.Authigenic clays, which grow in the pores from minerals in the connatewater, can be pore-filling or pore-lining. These clays have considerablesurface area exposed in the pore and can be reactive, while detritalclays that are part of the matrix are usually less reactive.Additionally, clays can be cementing, or grain-binding, materials thatreact with water or acid to disaggregate the formation if they are notprotected by quartz overgrowths. The most common clays that create clayproblems in hydrocarbon reservoirs are kaolinite, smectite, illite andchlorite.

Clays in formation rock can significantly impact estimation ofhydrocarbon reserves and the methods and costs for producing the storedhydrocarbons from formation rock. In evaluating unconventional formationrock such as shale that contains clays, determining clay type andcomposition is a significant challenge considering the chemicalcomplexity and heterogeneous nature of the unconventional formationrock.

In the downhole environment, gamma ray logs have been traditionally usedto estimate formation shale/clay volume based on correlation related toinherent elements such as potassium, uranium and thorium.

Combining density, neutron and spectral natural gamma ray logs, one candetermine clay density and neutron response to 100% clay at every depthand also determine clay volume independent of the clay types. Combiningsuch knowledge allows the calculation of CEC and hydrogen index (HI),and from the CEC vs. HI crossplot, clay typing is possible. Nonetheless,such measurements do not have enough sensitivity to allow for thedetermination of complex mineralogy.

In a laboratory setting, there are multiple techniques for elemental andmineralogical analysis for rock samples, most important are X-rayfluorescence (XRF), X-ray diffractometry (XRD), Fourier transforminfrared spectroscopy (FTIR), and Diffuse reflectance infraredFourier-transform spectroscopy (DRIFTS) in addition to qualitative thinsection analysis under polarized transmitted light. In all cases, it isnecessary to prepare the required samples to certain standards.

XRF is an electroscopic technique that employs a secondary orfluorescent emission generated by exciting a sample with a source ofX-radiation. The energy absorbed by the atoms cause the production ofsecondary x-ray and fluorescence, emitted by the sample. The intensityof these secondary x-rays is proportional to the concentration of eachelement in the sample. XRF allows fast identification of elementsheavier than Lithium (Z=3) in theory. But in practice, it is oftendifficult to quantify elements lighter than Sodium (Z=11). Currently,portable fluorescence devices allow real-time measurements, whichtranslate to fast decision making on the field. They offer thepossibility to analyze samples without the need of any previouspreparation. Despite the advances in portable devices, the accuracy thatcan be expected from its results is still lower than the one that can beobtained by means of laboratory analytic techniques although it can beenhanced using reference samples. It is also noted that XRF measureselements, converting elements to minerals is an inversion process whichmay require calibrations and boundary conditions to ensure convergingsolutions.

In XRD, when an x-ray diffraction beam strikes the surface, the matterabsorbs the radiation to a greater or lesser extent, depending on thedifferent mechanisms of interaction that are registered, eitherfluorescent type or disperse radiations. The dispersive one constitutedby the fraction of the incident energy that is emitted again withoutchanging its wavelength. X-ray diffraction is a particular case of thistype of radiation. The constant distances of each crystalline structureoriginate a characteristic distribution of maximums (peaks), whichallows identifying the crystals qualitatively; the intensity of thesepeaks is proportional to the number of planes that diffract the incidentbeams, what happens just with certain angles of incidence. In such acase, a semi-quantitative concentration of a specific structure can beobtained by analyzing the area under the curve. Each peak is assigned toan intensity value and, with this information, the crystal or crystalsto which the diffraction pattern investigated belongs are identified.Peaks of some minerals overlap and therefore depending on theirconcentration, minerals with little concentration can be completelymischaracterized with others. This can create large quantificationerrors in sample mineral analysis which can be above 20% depending onthe characterized mineral. In addition, for low angle diffraction lines(peaks) such as clays, the signal to noise ratio is usually poor, anddue to the amount of disorders that can occur in clays structures,quantifying clays accurately is often difficult. It is also noted thatXRD works only for crystalline, not for amorphous materials.

FTIR have been extensively used for mineral identification. Generally,sample preparation is extensive for FTIR. Specifically, potassiumbromide (KBr) is mixed with specific mesh size sample powder and thenpressed into a pellet and heated at 120° C. to remove water. Data isusually recorded in the range of 4000 to 400 cm⁻¹ using a spectralresolution of 4 cm⁻¹. FTIR can provide chemical and mineral informationon complex samples such as shales without the need for separating clays.Although FTIR is a powerful technique for mineral characterization, itdoes have some limitations with respect to clay quantification.

DRIFTS has recently been introduced as a commercial service for materialmineralogy characterization. It is a fast and efficient technique forquantifying mineralogy from OBM-free core and cuttings samples. DRIFTScan be used in a laboratory or at a wellsite. It is not a downholetechnique. It measures the vibrational absorbance due to chemical bondsand is capable of resolving illite, smectite, kaolinite, and chlorite.Samples are usually scanned as bulk powders without dilution inreflectance mode and spectra are collected over the mid-IR range from375 to 4000 cm⁻¹ with 4 cm⁻¹ resolution.

SUMMARY

This summary is provided to introduce a selection of concepts that arefurther described below in the detailed description. This summary is notintended to identify key or essential features of the claimed subjectmatter, nor is it intended to be used as an aid in limiting the scope ofthe claimed subject matter.

In accordance with the subject disclosure, methods and systems areprovided for clay detection, clay typing, and clay volume quantificationusing downhole electromagnetic measurements on a subsurface formationconducted by a logging tool. The downhole electromagnetic measurementsare conducted at a low frequency less than 5000 Hertz (“Hz”) and used todetermine and store permittivity data that characterizes permittivity ofthe formation at the low frequency less than 5000 Hz. The downhole lowfrequency electromagnetic measurements are nondestructive, and theresults indicate the methods and systems are highly sensitive to theexistence of clays.

In embodiments, the methods and systems can employ a computational modelthat relates a parameter extracted from measurement of permittivity of aformation at a low frequency less than 5000 Hz to data thatcharacterizes at least one clay type and corresponding clay volumefraction. Downhole electromagnetic measurements can be conducted on aformation of interest at a low frequency less than 5000 Hz, and theresults of such downhole electromagnetic measurements can be used todetermine and store permittivity data that characterizes permittivity ofthe formation of interest at the low frequency less than 5000 Hz. Aparameter can be extracted from the permittivity data. The extractedparameter can be used as input to the computational model, wherein thecomputational model outputs data that characterizes at least one claytype and corresponding clay volume fraction for the formation ofinterest. The data that characterizes at least one clay type andcorresponding clay volume fraction for the formation of interest asprovided by the computational model can be stored or output for use,such as for use in evaluating the formation of interest.

In embodiments, the computational model can be derived by measuringpermittivity of formations of different known clay types and differentclay volume fractions at the low frequency less than 5000 Hz (such asmultiple low frequencies less than 5000 Hz) and correlating a parameterextracted from the resultant permittivity to data that characterizes atleast one clay type and corresponding clay volume fraction.

In embodiments, the computational model can relate a parameter extractedfrom measurement of permittivity of a formation at multiple lowfrequencies less than 5000 Hz to data that characterizes at least oneclay type and corresponding clay volume fraction. The permittivity dataof the formation of interest as well as the parameter extracted from thepermittivity data can be derived from the electromagnetic measurementsof permittivity of the formation of interest at the multiple lowfrequencies less than 5000 Hz.

In embodiments, the multiple low frequencies less than 5000 Hz cancomprise at least three frequencies less than or equal to 100 Hz (andpossibly a set of at least three frequencies between 100 Hz and 1 Hz).

In embodiments, the parameter of the computational model as well as theparameter extracted from the measurement of permittivity of theformation of interest can be selected from the group consisting of afrequency-specific slope, a frequency-specific permittivity, a criticalfrequency where the measurement of permittivity of the formation ofinterest diverges from permittivity of a formation that does not haveclay, and combinations thereof.

In embodiments, the at least one clay type can be selected from thegroup consisting of kaolinite, smectite, illite, chlorite, andcombinations thereof.

In embodiments, the porous media sample can be a formation rock sample.In this case, the data that characterizes at least one clay type andcorresponding clay volume fraction as output from the computationalmodel can be used to calculate a value of cation exchange capacity (CEC)for the formation rock sample. The data that characterizes at least oneclay type and corresponding clay volume fraction as output from thecomputational model can also be used for evaluation of a hydrocarbonreservoir corresponding to the formation rock sample.

In embodiment, the extracting of the parameter can be performed by aprocessor. The computational model can also be embodied by a processor.The operations of the method or system or parts thereof can also becontrolled by a processor.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject disclosure is further described in the detailed descriptionwhich follows, in reference to the noted plurality of drawings by way ofnon-limiting examples of the subject disclosure, in which like referencenumerals represent similar parts throughout the several views of thedrawings, and wherein:

FIG. 1 is a schematic diagram illustrating an electrical double layermodel of interfacial polarization of clay particles;

FIG. 2 depicts plots of relative permittivity ε_(r) computed as afunction of frequency (in Hz) for a shaly sandstone formation rock underdifferent saturation conditions (including 10% water saturation/90% oilsaturation, 40% water saturation/60% oil saturation, 60% watersaturation/40% oil saturation, 80% water saturation/20% oil saturation,and 100% water saturation/0% oil saturation);

FIG. 3 depicts plots of relative permittivity ε_(r) computed as afunction of frequency (in Hz) for quartz mixed with smectite ofdifferent volume fractions (including 0.1%, 0.5%, 1%, 2%, 5%, 10%, 30%and 50%). For comparison, the relative permittivity ε_(r) computed as afunction of frequency for clean sandstone (pure quartz) is also plotted.All the curves have a water saturation level of 20% and an oilsaturation level of 80%;

FIG. 4 depicts a correlation curve that represents a relationshipbetween relative permittivity at 1 Hz (ε_(r), 1 Hz) and a volumefraction of smectite clay (V_(smectite)) using data extracted from themeasurements of FIG. 3;

FIG. 5 depicts a plot of relative permittivity ε_(r) computed as afunction of frequency (in Hz) for sandstone samples mixed with differentclay types (kaolinite, illite, chlorite and smectite) of the same 20%volume fraction with a total porosity of 30%. For comparison, therelative permittivity ε_(r) computed as a function of frequency for aclean sandstone sample (no clay) is also plotted. All the curves have awater saturation level of 20% and an oil saturation level of 80%;

FIG. 6A is a block diagram of a well logging system that can incorporateaspects of the subject disclosure;

FIG. 6B is a schematic illustration of a logging tool that can be partof the well logging system of FIG. 6A;

FIG. 7 is a flowchart illustrating a methodology for clay detection,clay typing, and clay volume quantification using a logging tool (suchas the logging tool of FIGS. 6A and 6B) for downhole electromagneticmeasurements performed on a formation of interest and dispersed at lowfrequencies (below 5000 Hz). The downhole electromagnetic measurementsare used to determine permittivity of the formation of interest at thelow frequencies (below 5000 Hz);

FIG. 8 is block diagram of a computer processing system, which can beused to embody parts of the methodology for clay detection, clay typing,and clay volume quantification as described herein;

FIG. 9 is a schematic diagram illustrating the combination of mineralogydata obtained from low-frequency downhole electromagnetic measurementswith other minerology log data (such as minerology data obtained fromconventional nuclear-based measurements) in order to characterize andevaluate lateral reservoir geological heterogeneity in a verticalwellbore environment.

DETAILED DESCRIPTION

The particulars shown herein are by way of example and for purposes ofillustrative discussion of the embodiments of the subject disclosureonly and are presented in the cause of providing what is believed to bethe most useful and readily understood description of the principles andconceptual aspects of the subject disclosure. In this regard, no attemptis made to show structural details in more detail than is necessary forthe fundamental understanding of the subject disclosure, the descriptiontaken with the drawings making apparent to those skilled in the art howthe several forms of the subject disclosure may be embodied in practice.Furthermore, like reference numbers and designations in the variousdrawings indicate like elements.

Interfacial polarization can be observed in formation rock containingclays (such as shaly sands) and in other porous media containing clays.When the surface of a nonconductive mineral, such as clay minerals andsilica grains, are exposed to electrolytes, it acquires charges due toionic adsorption, protonation/deprotonation of the hydroxyl groups, anddissociation of other potentially active surface groups. In the presenceof an external electromagnetic (EM) field, these surface charges formelectric dipoles and cause interfacial polarization (IP) effects. Thestrength of the IP effects is regulated by permittivity of the formationrock or other porous media.

The present disclosure provides a framework or methodology for claydetection, clay typing, and clay volume quantification using a downholelogging tool for downhole electromagnetic measurements on a formation ofinterest that are dispersed at low frequencies (below 5000 Hz). The lowfrequency downhole electromagnetic measurements are used to determinepermittivity of the formation of interest at the low frequencies (below5000 Hz). The low frequency downhole electromagnetic are nondestructive,and the results indicate the methodology is with high sensitivity to theexistence of clays compared to other conventional specializedmineralogical and elemental clay quantification methods such as x-raydiffraction, x-ray fluorescence, and Fourier-transform infraredspectroscopy.

In embodiments, clay minerals can be lumped together on the basis ofmolecular structure and composition into four commonly encountered andrepresentative clay types: kaolinite, illite, chlorite and smectite.Although each one of these clay types impacts formation conductivitydifferently, the fundamental mechanism is similar. When the surface of aclay mineral grain is exposed to electrolytes, it acquires charges dueto ionic adsorption, protonation/deprotonation of the hydroxyl groups,and dissociation of other potentially active surface groups.

Under an external electromagnetic (EM) field, both electrical conduction(due to charge carries) and interfacial polarization (due to surfacecharges) influences the measured EM fields. Electrical conductiondescribes the movement of the charge carries under the influence of theexternal EM field. This is a well understood phenomena and can bedescribed by Ohm's law.

The polarization of clay particles is mostly due to charge accumulationand movements at host-inclusion interfaces. The most common theory todescribe the interfacial polarization is the electrical double layershown in FIG. 1. At the surface of the clay particles, both Stern anddiffuse layers are formed due to charge absorptions and movements. Inthe presence of an applied external electromagnetic field, the doublelayer develops a counter ion cloud and diffused-charge distributionaround host-inclusion interfaces. Dynamics of accumulation/depletion ofcharge concentrations around host-inclusion interfaces influence themagnitude and phase of the EM response of the formation rock containingclay minerals or other porous media containing clay minerals. Theelectrical conductivity of the porous media can be described by acomplex conductivity σ given by:σ=σ^(R) +iωε ₀ε_(r), and  Eqn. (1a)σ=σ^(R) +iωε _(eff)  Eqn. (1b)

where σ^(R) is the in-phase component and ωε₀ε_(r) is the quadrature(out-phase) component of a complex conductivity σ, respectively;

-   -   ω is frequency,    -   ε₀ is the permittivity of vacuum (e.g., 8.854×10⁻¹² F m¹),    -   ε_(r) is the relative permittivity, and    -   ε_(eff) is the effective permittivity given by the product ε₀        ε_(r).

For a porous media containing clay minerals, the relative permittivityε_(r) and the effective permittivity ε_(eff) depend on clay type andvolume of each clay type. In addition, the relative permittivity ε_(r)and the effective permittivity ε_(eff) can be calculated based oneffective media theory. In embodiments, the EM response signal of theporous media can be detected by one or more receiver antennae (e.g.,receiver coil(s)). Phased-lock detection and amplification of the EMresponse signal can determine the amplitude and phase of the voltagelevels detected by the one or more receiver antennae. By recording andprocessing the amplitude and phase of such voltage levels, a measurementof the complex conductivity σ of the porous media can be obtained. Therelative permittivity ε_(r) and/or the effective permittivity ε_(eff) ofthe porous media can be extracted from the quadrature component of thecomplex conductivity σ according to eqns. (1a) and (1b) as set forthabove.

FIG. 2 shows relative permittivity ε_(r) (no unit) computed as afunction of frequency (in Hz) for a shaly sandstone formation rock underdifferent saturation conditions (including 10% water saturation/90% oilsaturation, 40% water saturation/60% oil saturation, 60% watersaturation/40% oil saturation, 80% water saturation/20% oil saturation,and 100% water saturation). The shaly sandstone formation rock has 90%quartz and 10% smectite with a total porosity of 30%. Very strongdispersion effects are observed below 1000 Hz. The largest permittivityvalue can reach 2×10⁴ (F m¹) at 1 Hz. For frequencies larger than 1000Hz, the permittivity has much smaller values and is non-dispersive. Notethat while parts of the curves show dependence on saturations, thedispersion section of the curves, for example in frequencies less than100 Hz, is very insensitive to saturation. This implies that themeasurement of relative permittivity ε_(r) of formation rock as afunction of frequency, at low frequencies less than 100 Hz, is asensitive indicator for clay detection and quantification of clayvolumes irrespective of the saturation conditions of the formation rock.

FIG. 3 shows relative permittivity ε_(r) (no unit) computed as afunction of frequency (in Hz) for quartz mixed with smectite ofdifferent volume fractions (including 0.1%, 0.5%, 1%, 2%, 5%, 10%, 30%and 50%). For comparison, the relative permittivity ε_(r) (no unit)computed as a function of frequency for clean sandstone (pure quartz) isalso plotted. All the curves have a water saturation level of 20% and anoil saturation level of 80%. It is observed that while the cleansandstone has no dispersion effect, the shaly sandstone shows strongdispersion effects. Specifically, the relative permittivities of theformation rock samples at a low frequency such as 1 Hz strongly dependson the respective volume fractions of the smectite clay component in theformation rock samples. Even with 1% or less clay content, thedispersion curves show a huge difference from the clean sandstone,indicating the measurement of relative permittivity of the formationrock sample at a low frequency (such as, for example, 1 Hz) is a verysensitive technique to detect small amount of clays for characterizationof the clay components of the formation rock sample. In addition, thedescending values of the relative permittivities of the formation rocksamples at the low frequency (such as, for example, 1 Hz) are easilydifferentiable for different volume fractions of the smectite claycomponent in the formation rock samples. The sensitivity todifferentiate clean and slightly shaly rock appears to be an improvementover downhole nuclear elemental spectroscopy measurements as describedherein.

In order to quantify clay volume, a computational model can be builtthat relates measurements of relative permittivity (or effectivepermittivity) at one or more low frequencies less than 5000 Hz to clayvolume fraction. In embodiments, the computational model can relatemeasurements of relative permittivity (or effective permittivity) atmultiple (at least three) low frequencies (e.g. 1 Hz, 10 Hz and 100 Hz)to clay volume fraction. For example, measurements of relativepermittivity of a formation rock sample at 1 Hz can be related to clayvolume fraction as shown in the correlation curve of FIG. 4, which showsa relationship between relative permittivity at 1 Hz (ε_(r), 1 Hz) and avolume fraction of smectite clay (v_(smectite)) using data extractedfrom the measurements of FIG. 3. This relationship demonstrates thesensitivity of low frequency permittivity on smectite clay volume andcan be represented by:

$\begin{matrix}{ɛ_{r},{{1\mspace{14mu}{Hz}} = {{1856.7*V_{smectite}} + 7.0}},{or}} & {{Eqn}.\mspace{14mu}\left( {2a} \right)} \\{v_{smectite} = {\frac{ɛ_{r,{1\mspace{14mu}{Hz}}} - ɛ_{r,{{clean}\mspace{14mu}{rock}}}}{Slope} = \frac{ɛ_{r,{1\mspace{14mu}{Hz}}} - 7.0}{1856.7}}} & {{Eqn}.\mspace{14mu}\left( {2b} \right)}\end{matrix}$

Note that the relationship of FIG. 4 and Eqns. (2a) and (2b) is anexample, and thus other correlations and computational models can beused depending on the clay type, clay volume and a mixture of differentclay types and clay volumes. Laboratory measurements of relativepermittivity (or effective permittivity) at the low frequency(ies) onreservoir rock samples of known or measured clay volumes can be used todetermine the relationship between measured permittivity at one or morelow frequencies and clay volume fractions individually or in mixtures.

FIG. 5 shows relative permittivity ε_(r) (no unit) computed as afunction of frequency (in Hz) for sandstone samples mixed with differentclay types (kaolinite, illite, chlorite and smectite) of the same 20%volume fraction with a total porosity of 30%. For comparison, therelative permittivity ε_(r) (no unit) computed as a function offrequency for a clean sandstone sample (no clay) is also plotted. Allthe curves have a water saturation level of 20% and an oil saturationlevel of 80%. It is clearly observed that smectite and kaolinite havedifferent relative permittivities at a low frequency such as 1 Hz, whichare easily distinguishable from one another. More specifically, thesandstone sample mixed with kaolinite at 20% volume fraction has arelative permittivity on the order of 40, while the sandstone samplemixed with smectite at 20% volume fraction has a relative permittivityε_(r) on the order of 2.5×10⁴. Furthermore, it is clearly observed thatsmectite and kaolinite have different critical frequencies where themeasured relative permittivity deviates significantly from that of cleansandstone. More specifically, the sandstone sample mixed with kaoliniteat 20% volume fraction has a critical frequency at or near 10 Hz, whilethe sandstone sample mixed with smectite at 20% volume fraction has acritical frequency near 900 Hz. Note that these two parameters of themeasured permittivity at a low frequency cannot be used to distinguishthe illite and chlorite clay types because these two clay types havealmost the same electric properties. In practical applications,permittivities at multiple low frequencies (e.g. 1 Hz, 10 Hz and 100 Hz)can be measured on core samples of the known different clay types(including the kaolinite, illite, chlorite, smectite and combinations ofsuch clay types) and of known different clay type volume fractions, andsuch measurements can be processed in order to build a computationalmodel (such as a correlation model) that relates parameters extractedfrom low frequency permittivity measurements into one or more clay typesand corresponding clay type volume fractions.

The following conclusions can be based on FIGS. 2, 3, 4 and 5.

First, measurements of relative permittivity ε_(r) (or effectivepermittivity ε_(eff)) as a function of frequency at low frequencies havealmost no dependence on water saturation of shaly sandstone formationrock. This makes such measurements an ideal clay detector for allsaturation conditions, pay zones or otherwise.

Second, the measurements of relative permittivity ε_(r) (or effectivepermittivity ε_(eff)) of the formation rock at low frequencies (such asless than 1000 Hz) strongly depends on clay volume fractions for shalysandstone formation rock. Even with 1% or less clay content, therelative permittivity ε_(r) (or the effective permittivity ε_(eff)) ofthe formation rock at the low frequency shows a huge difference fromthat of clean sandstone. In addition, for formation rocks with differentclay volume fractions, the relative permittivities (or effectivepermittivities) of the formation rocks at low frequency are easilydifferentiable for the different clay volume fractions.

Third, the measurements of relative permittivity ε_(r) (or effectivepermittivity ε_(eff)) of formation rock at low frequencies (such as lessthan 1000 Hz) also strongly depends on clay types for shaly sandstoneformation rock. Furthermore, the presence of smectite and kaolinite claytypes that commonly occur in a reservoir environment can be detectedbased on the measurements of relative permittivity ε_(r) (or effectivepermittivity ε_(eff)) of formation rock at low frequency (such as lessthan 1000 Hz). For example, the measurement of relative permittivityε_(r) (or effective permittivity ε_(eff)) of formation rock at a lowfrequency such as 1 Hz and/or the critical frequency that relativepermittivity ε_(r) (or effective permittivity ε_(eff)) of formation rockdeviates from that of clean sandstone can be used to detect the presenceof smectite and kaolinite clay types. Note that these measurementscannot distinguish between illite and chlorite clay types as these twoclays have almost the same electric properties.

Fourth, in order to detect different clays types (including smectite,kaolinite, illite and chlorite and combinations thereof) and to quantifythe corresponding volume fraction of the different clay types, relativepermittivity ε_(r) (or effective permittivity ε_(eff)) of the formationrock can be measured at multiple low frequencies below 5000 Hz. One ormore parameters (such as a frequency-specific slope orfrequency-specific measure(s) of permittivity) can be extracted from themultiple low frequency permittivity measurements and used in conjunctionwith a computational model (such as a correlation model) that relatessuch parameters to one or more clay types (such as smectite, kaolinite,illite, chlorite, and combinations thereof) and corresponding clay typevolume fractions.

During the exploration and production of oil and gas, many well loggingtechniques can be deployed to log data of a subsurface formation. Thedata contain information that can be used to locate subsurfacehydrocarbon reservoirs and to determine types and quantities ofsubsurface hydrocarbons. In such logging processes, a tool may belowered into a borehole traversing a subsurface formation, either afterthe well has been drilled or during the drilling process. A typicallogging tool includes a “sonde”, that emits, for example, acoustic or EMwaves to interact with the surrounding formation. The signals producedfrom such interactions are then detected and measured by one or moresensors on the instrument. By processing the detected signals, a profileor log of the formation properties can be obtained.

Logging techniques known in the art include “wireline” logging,logging-while-drilling (LWD), measurement-while-drilling (MWD), andlogging-while-tripping (LWT). Wireline logging involves lowering aninstrument into an already-drilled borehole at the end of an electricalcable to obtain measurements as the instrument is moved along theborehole. LWD and MWD involve disposing an instrument in a drillingassembly for use while a borehole is being drilled through earthformations. LWT involves disposing sources or sensors within the drillstring to obtain measurements while the string is being withdrawn fromthe borehole.

Turning now to FIG. 6A, a wireline logging system 1 is shown thatincludes an electromagnetic (EM) logging tool 10 which is suspended viaa cable 12 in a borehole 14 which traverses a formation 15. The cable 12is wound about a winch 17 or suitable suspension means located at thesurface of the earth formation, and may be utilized, if desired, tocarry data (information) which is sent by the tool 10 to a processor 20.

As is well known in the art, the gathered data may be preprocesseddownhole by a processor (not shown) associated with the tool 10 and maybe sent via the cable 12, or via wireless mechanisms (e.g., mud pulsing)to the surface-located processor 20 for additional processing. Theprocessor 20 may be located in the vicinity of the formation 15 or atanother site as desired. Alternatively, raw data may be sent to theprocessor 20. As has been previously established, the mudcake on theborehole wall may be relatively conductive in the case where water-basemud is used in the borehole, or may be relatively resistive in the casewhere oil-base mud is used in the borehole. It is desirable that thetool 10 can be configured for use in both situations. In otherembodiments, the logging tool 10 can be an LWD logging tool or MWDlogging or LWT logging tool.

EM logging tools are widely used for formation logging applications. EMlogging tools include antennas that are operable as transmitters and/orreceivers. The antennas are typically solenoid coils. During operation,a coil may function as a transmitter antenna when it is energized withan alternating current or an oscillating electrical signal. Thetransmitter antenna emits EM waves through the borehole mud and into thesurrounding earth formation. The same coil or another coil may functionas a receiver antenna that collects EM signals carrying informationabout the interactions between the EM waves and the mud/formation.

In conventional EM logging tools, the transmitter and receiver antennasare mounted with their axes aligned with the longitudinal axis of theinstrument. Thus, these tools are implemented with antennas havinglongitudinal magnetic dipoles (LMD). When an LMD antenna is placed in aborehole and energized to transmit EM energy, the induced electriccurrents flow around the antenna in the borehole and in the surroundingearth formations, and no net current flows up or down the borehole.

More recent EM well logging tools have tilted or transverse coils, i.e.,the coil's axis is not parallel with the longitudinal axis of thesupport. Consequently, the antenna has a transverse or tilted magneticdipole (TMD). The TMD configuration permits a tool to have athree-dimensional evaluation capability, such as information aboutresistivity anisotropy or locations and orientations of dips and faults.In addition, directional sensitivity of the data can be used fordirectional drilling. Under certain conditions, a TMD-antenna may causea net current to flow up or down the borehole. Some TMD-antennas areconfigured with multiple coils. For example, a particular TMD-antennadesign includes a set of three coils, and such an antenna is known as atriaxial antenna.

In wireline applications, the antennas are typically enclosed in ahousing made of tough non-conductive materials such as a laminatedfiberglass material. In LWD applications, the antennas are generallyencased into a metallic support so that it can withstand the hostileenvironment and conditions encountered during drilling. Alternatively,logging instruments may be made of composite materials, thus, providinga non-conductive structure for mounting the antennas.

Induction logging is a well-known form of EM logging. In this type oflogging, induction tools are used to produce a conductivity orresistivity profile of earth formations surrounding a borehole.

A conventional induction logging tool or “sonde” may include atransmitter antenna and a receiver antenna. Note that the designation ofa transmitter and a receiver is for clarity of illustration. One skilledin the art would appreciate that a transmitter may be used as a receiverand a receiver may also be used as a transmitter depending on theapplication. Each antenna may include one or more coils, and may bemounted on the same support member or on different support members,i.e., the transmitter antenna and the receiver antenna may be ondifferent tool sections. The antennas are axially spaced from each otherin the longitudinal direction of the tool.

In use, the transmitter antenna is energized with an alternatingcurrent. This generates an EM field that induces eddy currents in theearth formation surrounding the borehole. The intensity of the eddycurrents is proportional to the conductivity of the formation. The EMfield generated by the eddy currents, in turn, induces an electromotiveforce in one or more receiving coils. Phase-locked detection,amplification, and digitization of this electromotive force signaldetermines the amplitude and the phase of the voltage on the receivercoil. By recording and processing the receiver voltages, a measurementof complex conductivity of the earth formation can be obtained. Therelative permittivity and/or the effective permittivity of the earthformation can be extracted from the quadrature component of the complexconductivity according to eqns. (1a) and (1b) as set forth above.

FIG. 6B illustrates an induction logging tool 10 that includes atransmitter coil 110, a receiver coil 112 for probing a shallow depthinto the formation, a receiver coil 115 for probing a medium depth intothe formation, and a receiver coil 118 for probing deeper into theformation. Buckling coils or trim coils 111, 114, 113, 116, 117 and 119are provided to eliminate or reduce direct coupling between thetransmitter coil 110 and the receiver coils 112, 115 and 118. Inaddition, the logging tool 10 can also include one or more electrodes(one shown as 120 in FIG. 6B), such as those used in conventionalconductivity/resistivity tools. Details of the induction logging tool 10are set forth in U.S. Pat. No. 7,501,829, commonly assigned to assigneeof the subject application and herein incorporated by reference in itsentirety. The transmitter antenna coil 110 can be energized with analternating current of desired frequency (in Hz). This generates an EMfield that induces eddy currents in the earth formation surrounding theborehole. The intensity of the eddy currents is proportional to theconductivity of the formation. The EM field generated by the eddycurrents, in turn, induces an electromotive force in the receiver coils112, 115, 118. Phase-locked detection, amplification, and digitizationof this electromotive force signal determines the amplitude and thephase of the voltage on the respective receiver coils 112, 115, 118. Byrecording and processing the receiver voltage signal sensed by thereceiver coils 112, 115, 118, measurements of complex conductivity ofthe earth formation can be obtained for three different radial depths(shallow/medium, deep) into the formation. The relative permittivityand/or the effective permittivity of the earth formation at therespective depth locations (shallow, medium, deeper) can be extractedfrom the quadrature component of the complex conductivity measurementsaccording to eqns. (1a) and (1b) as set forth above.

FIG. 7 depicts a workflow for clay detection, clay typing, and clayvolume quantification using a logging tool (such as the logging tool ofFIGS. 6A and 6B) for downhole electromagnetic measurements performed ona formation of interest and dispersed at low frequencies (below 5000Hz). The downhole electromagnetic measurements are used to determinepermittivity of the formation of interest at the low frequencies (below5000 Hz).

In block 701, a computational model is built (or provided) that relatesrelevant parameters extracted from low frequency permittivity data(which is data derived from low frequency electromagnetic measurementsthat are used to determine permittivity of a formation) to datacharacterizing clay type(s) and clay volume fraction(s) of theformation. The computational model can be built by configuring adownhole EM logging tool (such as the logging tools of FIGS. 6A and 6B)to conduct downhole electromagnetic measurements and determinepermittivity of formations of known different clay types (includingkaolinite, illite, chlorite, smectite and combinations of such claytypes) and of known different clay type volume fractions at multiple lowfrequencies (e.g. 1 Hz, 10 Hz and 100 Hz). Such low frequency downholeelectromagnetic measurements can measure complex conductance of theformation. Permittivity data can be determined from the complexconductance of the formation. The permittivity data can representeffective permittivity or relative permittivity of the formation. Thepermittivity data that results from such low frequency downholeelectromagnetic measurements can be processed to extract one or morerelevant parameters from the low frequency permittivity data (such as afrequency-specific slope or frequency-specific measure(s) ofpermittivity or critical frequency) and determine a correlation functionor other data structure that describes the relationship between theextracted relevant parameter(s) and one or more clay types andcorresponding clay type volume fractions. The correlation function orother data structure can then be integrated as part of the computationalmodel. The computational model can be designed to take as input one ormore relevant parameters extracted from low-frequency permittivity data.The correlation function or other data structure of the computationalmodel is used to output one or more clay types and corresponding claytype volume fractions that correspond to the relevant parameter(s)supplied as input to the computational model.

Although current laboratory techniques do not have the capabilities todetect low concentration of clays, calibration curves can be built basedon artificial cores where a known amount of clay is synthesized andprepared for the different clay types as individual and in a mixed knownamount. Resistivity measurements can be conducted and at differentfrequencies and a calibration curve/model can be built and used toidentify clay and its volume. A similar or close calibration curve ormodel can be produced using the current available inversion models.

In block 703, a downhole EM logging tool (such as the EM logging tool ofFIGS. 6A and 6B) is configured to measure and store conductivity datarepresenting conductivity of a formation of interest at multiple lowfrequencies less than 5000 Hz (e.g. 1 Hz, 10 Hz and 100 Hz). In thisblock 702, the downhole EM logging tool can be located in a desireddepth location in a borehole that traverses the formation of interestand operated to measure complex conductivity data representingconductivity of the formation of interest at the multiple lowfrequencies less than 5000 Hz (e.g. 1 Hz, 10 Hz and 100 Hz). For eachone of the multiple low frequencies (e.g. 1 Hz, 10 Hz and 100 Hz) of theexperiment, the frequency of the applied time varying externalelectromagnetic field produced by the transmitter antenna of thedownhole EM logging tool can be controlled to correspond to theparticular low frequency, and the electromagnetic response of theformation of interest can be measured by the downhole EM logging toolfor that particular low frequency.

In block 705, the conductivity data measured and stored in block 703 isprocessed to extract permittivity data representing permittivity of theformation of interest at the multiple low frequencies less than 5000 Hz(e.g. 1 Hz, 10 Hz and 100 Hz). Such permittivity data can representeffective permittivity or relative permittivity of the formation ofinterest at the multiple low frequencies less than 5000 Hz (e.g. 1 Hz,10 Hz and 100 Hz). For example, the relative permittivity and/or theeffective permittivity of the formation of interest can be extractedfrom the quadrature component of the complex conductivity of theformation of interest according to Eqn. (1a) and (1b) as set forthabove.

In blocks 707, 709, 711 and 713, the permittivity data measured andstored in block 705, which represents permittivities of the formation ofinterest as a function of frequency, can be analyzed for clay detectionand qualitative clay assessment. For example, the permittivity datameasured and stored in block 705 can be plotted in block 707 and theplot (or the permittivity data itself) can be analyzed for claydetection in block 709. For example, the plot (or the permittivity dataitself) can be compared to measured permittivity values (e.g. curves)for one or more formations that are known to not have clay as well as tomeasured permittivity values (e.g. curves) for one or more formations ofknown clay types. If the permittivity data measured and stored in block705 is dissimilar to the measured permittivity values (e.g. curves) forthe one or more formations that are known to not have clay and similarto the measured permittivity values (e.g. curves) for one or moreformations of known clay types, the process can determine that theformation does contain clays in block 711 and continue to blocks 713 to719. On the other hand, when the permittivity data measured and storedin block 705 is similar to measured permittivity values (e.g. curves)for the one or more formations that are known to not have clay anddissimilar to the measured permittivity values (e.g. curves) for the oneor more formations of known clay types, the process can determine inblock 711 that the formation does not contain clays and continue toblock 721.

In block 713, the permittivity data measured and stored in block 705 canbe analyzed for qualitative clay assessment. For example, thepermittivity data measured and stored in block 705 can be plotted andcompared to measured permittivity values (e.g. curves) for one or moreformations of known clay types. The known clay type of the formationwhose measured permittivity values best match the permittivity datameasured and stored in block 705 can be identified as the clay type forthe formation of interest.

In block 715, the permittivity data measured and stored in block 705 canbe processed to extract relevant parameter(s) of such data for input tothe computational model of block 701.

In block 717, the relevant parameter(s) extracted in 715 can be used asinput to the computational model, which outputs data characterizing oneor more clay types and corresponding clay volume fractions for theformation of interest. The data characterizing one or more clay typesand corresponding clay volume fractions for the formation of interest asoutput by the computational model can be stored in computer memory forsubsequent access or possibly output for analysis (for example, plottedas part of a well log).

In block 719, the process can identify the formation of interest asshaly type and possibly use the data that characterizes the one or moreclay types and corresponding clay volume fractions as output from thecomputational model in block 717 to evaluate the formation of interest.For example, the data that characterizes the one or more clay types andcorresponding clay volume fractions as output from the computationalmodel in block 717 can be used to calculate a value of cation exchangecapacity (CEC) for the formation of interest. The value of CECrepresents a quantity of positively charged ions (cations) that theformation of interest can accommodate on its negative charged surface.It is typically expressed as milliequivalents per 100 grams. The CECvalue can be used in formation modeling and simulation of the formationof interest.

For example, if both clay types and clay volume fractions are known foreach clay type, the total CEC values can be calculated using a mixinglaw as follows:CEC=w _(C)×CEC_(C) +w _(I)×CEC_(I) +w _(K)×CEC_(K) +w_(S)×CEC_(S),  Eqn. (3)where w_(C), w_(I), w_(K), w_(S) are clay weight fractions for chlorite,illite, kaolinite and smectite, respectively; and CEC_(C), CEC_(I),CEC_(K) and CEC_(S) are CEC values for chlorite, illite, kaolinite andsmectite.

The CEC value for each above-mentioned clay mineral is well defined andcan be treated as a known parameter. For example, the clay volume can becalculated as follows:

$\begin{matrix}{{V_{cl} = \frac{{\rho_{matrix}\left( {1 - {total}} \right)} \times W_{cl}}{\rho_{cl}}},} & {{Eqn}.\mspace{14mu}(4)}\end{matrix}$where ϕ_(total) is the formation total porosity, ρ_(matrix) is matrixdensity, W_(cl) is weight of clay, and ρ_(cl) is density of clay. Inblock 721, the process can identify the formation of interest asnon-shaly type for evaluation of the formation.

In other examples, for vertical wellbore environments, the mineralogydata that characterizes the one or more clay types and correspondingclay volume fractions as output from the computational model in block717 can be combined with other minerology log data (such as minerologydata obtained from conventional nuclear-based measurements) as part ofblock 719 in order to characterize and evaluate lateral reservoirgeological heterogeneity. This application is shown schematically inFIG. 9. Note that the mineralogy data output from the computationalmodel in block 717 can characterize the mineralogy of the formation rockat a deeper lateral (or radial) offset into the formation relative tothe vertical wellbore as compared to the mineralogy data of the otherminerology log data, which can characterize the mineralogy of theformation rock at a shallower lateral (or radial) offset into theformation relative to the vertical wellbore.

In yet other examples, for while-drilling applications in horizontalwellbore environments, the minerology data that characterizes the one ormore clay types and corresponding clay volume fractions as output fromthe computational model in block 717 can be used to identify shalyformation rock and control geosteering of the drill bit based thereon aspart of block 719 in order to avoid shaly formation rock and maximizereservoir contact and well performance.

FIG. 8 illustrates an example device 2500, with a processor 2502 andmemory 2504 that can be configured to implement various embodiments ofthe logging methodology and logging systems as discussed in thisdisclosure. Memory 2504 can also host one or more databases and caninclude one or more forms of volatile data storage media such asrandom-access memory (RAM), and/or one or more forms of nonvolatilestorage media (such as read-only memory (ROM), flash memory, and soforth).

Device 2500 is one example of a computing device or programmable deviceand is not intended to suggest any limitation as to scope of use orfunctionality of device 2500 and/or its possible architectures. Forexample, device 2500 can comprise one or more computing devices,programmable logic controllers (PLCs), etc.

Further, device 2500 should not be interpreted as having any dependencyrelating to one or a combination of components illustrated in device2500. For example, device 2500 may include one or more of computers,such as a laptop computer, a desktop computer, a mainframe computer,etc., or any combination or accumulation thereof.

Device 2500 can also include a bus 2508 configured to allow variouscomponents and devices, such as processors 2502, memory 2504, and localdata storage 2510, among other components, to communicate with eachother.

Bus 2508 can include one or more of any of several types of busstructures, including a memory bus or memory controller, a peripheralbus, an accelerated graphics port, and a processor or local bus usingany of a variety of bus architectures. Bus 2508 can also include wiredand/or wireless buses.

Local data storage 2510 can include fixed media (e.g., RAM, ROM, a fixedhard drive, etc.) as well as removable media (e.g., a flash memorydrive, a removable hard drive, optical disks, magnetic disks, and soforth).

One or more input/output (I/O) device(s) 2512 may also communicate via auser interface (UI) controller 2514, which may connect with I/Odevice(s) 2512 either directly or through bus 2508.

In one possible implementation, a network interface 2516 may communicateoutside of device 2500 via a connected network.

A media drive/interface 2518 can accept removable tangible media 2520,such as flash drives, optical disks, removable hard drives, softwareproducts, etc. In one possible implementation, logic, computinginstructions, and/or software programs comprising elements of module2506 may reside on removable media 2520 readable by mediadrive/interface 2518.

In one possible embodiment, input/output device(s) 2512 can allow a user(such as a human annotator) to enter commands and information to device2500, and also allow information to be presented to the user and/orother components or devices. Examples of input device(s) 2512 include,for example, sensors, a keyboard, a cursor control device (e.g., amouse), a microphone, a scanner, and any other input devices known inthe art. Examples of output devices include a display device (e.g., amonitor or projector), speakers, a printer, a network card, and so on.

Some of the methods and processes described above, can be performed by aprocessor. The term “processor” should not be construed to limit theembodiments disclosed herein to any particular device type or system.The processor may include a computer system. The computer system mayalso include a computer processor (e.g., a microprocessor,microcontroller, digital signal processor, or general-purpose computer)for executing any of the methods and processes described above.

The computer system may further include a memory such as a semiconductormemory device (e.g., a RAM, ROM, PROM, EEPROM, or Flash-ProgrammableRAM), a magnetic memory device (e.g., a diskette or fixed disk), anoptical memory device (e.g., a CD-ROM), a PC card (e.g., PCMCIA card),or other memory device.

Some of the methods and processes described above, can be implemented ascomputer program logic for use with the computer processor. The computerprogram logic may be embodied in various forms, including a source codeform or a computer executable form. Source code may include a series ofcomputer program instructions in a variety of programming languages(e.g., an object code, an assembly language, or a high-level languagesuch as C, C++, or JAVA). Such computer instructions can be stored in anon-transitory computer readable medium (e.g., memory) and executed bythe computer processor. The computer instructions may be distributed inany form as a removable storage medium with accompanying printed orelectronic documentation (e.g., shrink wrapped software), preloaded witha computer system (e.g., on system ROM or fixed disk), or distributedfrom a server or electronic bulletin board over a communication system(e.g., the Internet or World Wide Web).

Alternatively or additionally, the processor may include discreteelectronic components coupled to a printed circuit board, integratedcircuitry (e.g., Application Specific Integrated Circuits (ASIC)),and/or programmable logic devices (e.g., a Field Programmable GateArrays (FPGA)). Any of the methods and processes described above can beimplemented using such logic devices.

Although only a few examples have been described in detail above, thoseskilled in the art will readily appreciate that many modifications arepossible in the examples without materially departing from this subjectdisclosure. Accordingly, all such modifications are intended to beincluded within the scope of this disclosure as defined in the followingclaims. In the claims, means-plus-function clauses are intended to coverthe structures described herein as performing the recited function andnot only structural equivalents, but also equivalent structures. Thus,although a nail and a screw may not be structural equivalents in that anail employs a cylindrical surface to secure wooden parts together,whereas a screw employs a helical surface, in the environment offastening wooden parts, a nail and a screw may be equivalent structures.It is the express intention of the applicant not to invoke 35 U.S.C. §112, paragraph 6 for any limitations of any of the claims herein, exceptfor those in which the claim expressly uses the words ‘means for’together with an associated function.

What is claimed is:
 1. A method for characterizing clay content of asubsurface formation of interest, the method comprising: i) configuringand operating a downhole logging tool to conduct a downholeelectromagnetic measurement on the formation of interest at a lowfrequency less than 5000 Hertz, wherein the downhole electromagneticmeasurement is used to determine and store permittivity data thatcharacterizes permittivity of the formation of interest at the lowfrequency less than 5000 Hertz; ii) extracting a parameter from thepermittivity data that characterizes permittivity of the formation ofinterest at the low frequency less than 5000 Hertz; and iii) plottingthe permittivity data and comparing the permittivity data to one or morecurves of measured permittivity curves obtained from one or moreformations known to not have clay, and determining if the formation hasclay, and if the formation is determined to have clay, comparing theplot of permittivity data to curves of measured permittivity values ofone or more formations having clay types and identifying from thecomparison of the plot and curves the clay type for the formation ofinterest.
 2. The method according to claim 1, further comprising:storing or outputting the data that characterizes at least one clay typeand corresponding clay volume fraction for the formation of interest asprovided by the computational model in iii).
 3. The method according toclaim 1, wherein: the computational model of iii) is derived bymeasuring permittivity of formations of different known clay types anddifferent clay volume fractions at the low frequency less than 5000Hertz and correlating a parameter extracted from the resultantpermittivity to data that characterizes at least one clay type andcorresponding clay volume fraction.
 4. The method according to claim 1,wherein: the computational model of iii) is derived by measuringpermittivity of formations of different known clay types and differentclay volume fractions at multiple low frequencies less than 5000 Hertzand correlating a parameter extracted from the resultant permittivity todata that characterizes at least one clay type and corresponding clayvolume fraction.
 5. The method according to claim 1, wherein: thecomputational model of iii) relates a parameter extracted frommeasurement of permittivity of a formation at multiple low frequenciesless than 5000 Hertz to data that characterizes at least one clay typeand corresponding clay volume fraction; the permittivity data determinedand stored in i) as well as the parameter extracted from thepermittivity data in ii) are derived from electromagnetic measurementsconducted by the downhole logging tool on the formation of interest atthe multiple low frequencies less than 5000 Hertz.
 6. The methodaccording to claim 5, wherein: the multiple low frequencies less than5000 Hertz comprises at least three frequencies less than or equal to100 Hertz.
 7. The method according to claim 5, wherein: the multiple lowfrequencies less than 5000 Hertz comprises a set of at least threefrequencies between 100 Hertz and 1 Hertz.
 8. The method according toclaim 1, wherein: the parameter extracted from the permittivity data inii) and input to the computational model of iii) is selected from thegroup consisting of: a frequency-specific slope, a frequency-specificpermittivity, a critical frequency where permittivity of the formationof interest diverges from permittivity of a formation that does not haveclay, and combinations thereof.
 9. The method according to claim 1,wherein: the permittivity data of i) represents relative permittivity oreffective permittivity of the formation of interest.
 10. The methodaccording to claim 1, wherein: the permittivity data of i) is derivedfrom a quadrature component of a measurement of complex conductivity ofthe formation of interest.
 11. The method according to claim 1, wherein:the at least one clay type is selected from the group consisting of:kaolinite, smectite, illite, chlorite, and combinations thereof.
 12. Themethod according to claim 1, further comprising: using the data thatcharacterizes at least one clay type and corresponding clay volumefraction for the formation as output from the computational model iniii) to calculate a value of cation exchange capacity (CEC) for theformation.
 13. The method according to claim 1, further comprising:using the data that characterizes at least one clay type andcorresponding clay volume fraction for the formation of interest asoutput from the computational model in iii) for evaluation of theformation of interest.
 14. The method according to claim 1, furthercomprising: combining the data that characterizes at least one clay typeand corresponding clay volume fraction for the formation of interest asoutput from the computational model in iii) with other log data (such asconventional nuclear based mineralogy log data) for characterizing andevaluating near wellbore reservoir geological heterogeneity in avertical wellbore.
 15. The method according to claim 1, furthercomprising: using the data that characterizes at least one clay type andcorresponding clay volume fraction for the formation of interest asoutput from the computational model in iii) to identify shaly formationrock and control geosteering of the drill bit while drilling ahorizontal wellbore based thereon in order to avoid shaly formationrock.
 16. The method according to claim 1, wherein: the extracting ofthe parameter in ii) is performed by a processor.
 17. The methodaccording to claim 1, wherein: the computational model of iii) isembodied by a processor.
 18. A system for characterizing clay content ofa subsurface formation of interest, the system comprising: a downholelogging tool that is configured to conduct an electromagneticmeasurement on the formation of interest at a low frequency less than5000 Hertz, wherein the electromagnetic measurement measures and storesconductivity data that characterizes complex conductivity of theformation of interest at the low frequency less than 5000 Hertz; and atleast one processor that, when executing program instructions stored inmemory, is configured to: i) obtain the conductivity data thatcharacterizes complex conductivity of the formation of interest at thelow frequency less than 5000 Hertz; ii) process the conductivity data ofi) to determine permittivity data that characterizes permittivity of theformation of interest at the low frequency less than 5000 Hertz; iii)extract a parameter from the permittivity data of ii); and iv) plottingthe permittivity data and comparing the permittivity data to one or morecurves of measured permittivity curves obtained from one or moreformations known to not have clay, and determining if the formation hasclay, and if the formation is determined to have clay, comparing theplot of permittivity data to curves of measured permittivity values ofone or more formations having clay types and identifying from thecomparison of the plot and curves the clay type for the formation ofinterest.
 19. The system according to claim 18, wherein: the at leastone processor is further configured to store or output the data thatcharacterizes at least one clay type and corresponding clay volumefraction for the formation of interest as provided by the computationalmodel in iv).
 20. The system according to claim 18, wherein: thepermittivity data determined in ii) is derived from a quadraturecomponent of the complex conductivity of the formation of interest. 21.The system according to claim 18, wherein: the computational model ofiv) is derived by measuring permittivity of formations of differentknown clay types and different clay volume fractions at the lowfrequency less than 5000 Hertz and correlating a parameter extractedfrom the resultant permittivity to data that characterizes at least oneclay type and corresponding clay volume fraction.
 22. The systemaccording to claim 18, wherein: the computational model of iv) isderived by measuring permittivity of formations of different known claytypes and different clay volume fractions at multiple low frequenciesless than 5000 Hertz and correlating a parameter extracted from theresultant permittivity to data that characterizes at least one clay typeand corresponding clay volume fraction.
 23. The system according toclaim 18, wherein: the computational model of iv) relates a parameterextracted from measurement of permittivity of a formation at multiplelow frequencies less than 5000 Hertz to data that characterizes at leastone clay type and corresponding clay volume fraction; the permittivitydata determined in ii) as well as the parameter extracted from thepermittivity data in iii) are derived from electromagnetic measurementsconducted by the downhole logging tool on the formation of interest atthe multiple low frequencies less than 5000 Hertz.
 24. The systemaccording to claim 23, wherein: the multiple low frequencies less than5000 Hertz comprises at least three frequencies less than or equal to100 Hertz.
 25. The system according to claim 23, wherein: the multiplelow frequencies less than 5000 Hertz comprises a set of at least threefrequencies between 100 Hertz and 1 Hertz.
 26. The system according toclaim 18, wherein: the downhole logging tool is selected from the groupconsisting of: a wireline logging tool, a logging-while-drilling loggingtool, a measurement-while-drilling logging tool, and atripping-while-drilling logging tool.